期刊,Robotics & Machine Learning Daily News 2024年卷Oct.9期_国家学术搜索

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Robotics & Machine Learning Daily News

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New Findings Reported from University of Minnesota Describe Advances in Machine Learning (Developing a New Active Canopy Sensor- and Machine Learning-based In-s eason Rice Nitrogen Status Diagnosis and Recommendation Strategy)

144-145页

查看更多>>摘要：Research findings on Machine Learning are discussed in a new report. According to news reporting originating in St. Pa ul, Minnesota, by NewsRx journalists, research stated, "Traditional critical nit rogen (N) dilution curve (CNDC) construction for N nutrition index (NNI) determi nation has limitations for in-season crop N diagnosis and recommendation under d iverse on-farm conditions. This study was conducted to (i) develop a new rice (O ryza sativa L.) critical N concentration (Nc) determination approach using veget ation index-based CNDCs; and (ii) develop an N recommendation strategy with this new Ncdetermination approach and evaluate its reliability and practicality." Financial supporters for this research include Norwegian Ministry of Foreign Aff airs (SINOGRAIN III), Science and Technology Planning Project of Lhasa, United S tates Department of Agriculture (USDA).

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Researchers at King Mongkut's University of Technology Thonburi (KMUTT) Release New Data on Machine Learning (Effects of Spatial Microstructure Characteristics On Mechanical Properties of Dual Phase Steel By Inverse Analysis and Machine ... )

145-146页

查看更多>>摘要：A new study on Machine Learning is now available. According to news reporting out of Bangkok, Thailand, by NewsRx edit ors, research stated, "This work aims to investigate complex relationship betwee n microstructure characteristics and mechanical properties of dual phase (DP) st eel through an inverse analysis based on Markov chain Monte Carlo (MCMC) method combined with meso-scale material modelling. In this framework, a machine learni ng approach as surrogate model was developed, in which support vector regression (SVR) and artificial neural network (ANN) were trained using results from repre sentative volume element (RVE) simulations coupled with damage model, which were previously calibrated with experimental data of commercial DP steel grades." Funders for this research include Petchra Pra Jom Klao Master's Degree Research Scholarship, Reserach Strengthening Project of the Faculty of Engineering from K ing Mongkut's University of Technology Thonburi, Thailand Advance Institute of S cience and Technology-Tokyo Institute of Technology (TAISTTokyo Tech), Thailand Science Research and Innovation (TSRI), National Science, Research and Innovati on Fund (NSRF), National Research Council of Thailand (NRCT).

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Study Findings from Worcester Polytechnic Institute Broaden Understanding of Rob otics (Guiding the Last Centimeter: Novel Anatomy-aware Probe Servoing for Stand ardized Imaging Plane Navigation In Robotic Lung Ultrasound)

146-147页

查看更多>>摘要：Current study results on Robotics have been published. According to news reporting from Worcester, Massachusetts, by N ewsRx journalists, research stated, "Navigating the ultrasound (US) probe to the standardized imaging plane (SIP) for image acquisition is a critical but operat or-dependent task in conventional freehand diagnostic US. Robotic US systems (RU SS) offer the potential to enhance imaging consistency by leveraging real-time U S image feedback to optimize the probe pose, thereby reducing reliance on operat or expertise." Financial support for this research came from National Institutes of Health (NIH ) - USA.

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Findings from Tsinghua University Provides New Data on Robotics (A Robust Robot Perception Framework for Complex Environments Using Multiple Mmwave Radars)

147-148页

查看更多>>摘要：Research findings on Robotics are disc ussed in a new report. According to news reporting out of Beijing, People's Repu blic of China, by NewsRx editors, research stated, "The robust perception of env ironments is crucial for mobile robots to operate autonomously in complex enviro nments. Over the years, mobile robots mainly rely on optical sensors for percept ion, which degrade severely in adverse weather conditions." Our news journalists obtained a quote from the research from Tsinghua University , "Recently, singlechip millimeter-wave (mmWave) radars have been widely used f or mobile perception, owing to their robustness to all-weather conditions, light weight design, and low cost. However, existing research based on mmWave radars p rimarily focuses on single radar and single task. Due to the limited field of vi ew and sparse observation, perception based on a single radar may not ensure the required robustness in complex environments. To address this challenge, we prop ose a novel robust perception framework for robots in complex environments based on multiple mmWave radars, named MMR-PFR. The framework integrates three critic al tasks for robots, including ego-motion estimation, multi-radar fusion mapping , and dynamic target state estimation. Multiple tasks collaborate and facilitate each other to improve overall performance. In the framework, we propose a new m ulti-radar point cloud fusion method to generate a more accurate environmental m ap. In addition, we propose a new online calibration algorithm for multiple rada rs to ensure the long-term reliability of the system. To evaluate MMR-PRF, we bu ild a prototype and carry out experiments in real-world scenarios."

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Researcher at Nanjing Normal University Publishes Research in Machine Learning ( Chemical Fractions and Magnetic Simulation Based on Machine Learning for Trace M etals in a Sedimentary Column of Lake Taihu)

148-149页

查看更多>>摘要：Current study results on artificial in telligence have been published. According to news reporting out of Nanjing, Peop le's Republic of China, by NewsRx editors, research stated, "In this study, the chemical fractions (CFs) of trace metal (TMs) and multiple magnetic parameters w ere analysed in the sedimentary column from the centre of Lake Taihu. The sedime ntary column, measuring 53 cm in length, was dated using 210Pb and 137Cs to be 1 24 years old." Funders for this research include The Natural Science Foundation of Jiangsu Prov ince, China; The National Natural Science Foundation of China; The Open Fund of Key Laboratory of Water Treatment of Taihu Lake Basin, Ministry of Water Resourc es.

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Medical University of Sofia Reports Findings in Artificial Intelligence (Artific ial intelligence as a tool in drug discovery and development)

149-150页

查看更多>>摘要：New research on Artificial Intelligenc e is the subject of a report. According to news reporting originating in Sofia, Bulgaria, by NewsRx journalists, research stated, "The rapidly advancing field o f artificial intelligence (AI) has garnered substantial attention for its potent ial application in drug discovery and development. This opinion review criticall y examined the feasibility and prospects of integrating AI as a transformative t ool in the pharmaceutical industry." The news reporters obtained a quote from the research from the Medical Universit y of Sofia, "AI, encompassing machine learning algorithms, deep learning, and da ta analytics, offers unprecedented opportunities to streamline and enhance vario us stages of drug development. This opinion review delved into the current lands cape of AI-driven approaches, discussing their utilization in target identificat ion, lead optimization, and predictive modeling of pharmacokinetics and toxicity . We aimed to scrutinize the integration of large-scale omics data, electronic h ealth records, and chemical informatics, highlighting the power of AI in uncover ing novel therapeutic targets and accelerating drug repurposing strategies. Desp ite the considerable potential of AI, the review also addressed inherent challen ges, including data privacy concerns, interpretability of AI models, and the nee d for robust validation in real-world clinical settings. Additionally, we explor ed ethical considerations surrounding AI-driven decision-making in drug developm ent. This opinion review provided a nuanced perspective on the transformative ro le of AI in drug discovery by discussing the existing literature and emerging tr ends, presenting critical insights and addressing potential hurdles."

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Researchers at China University of Geosciences Report New Data on Robotics (A Se lf-learning Memetic Algorithm for Human-robot Collaboration Scheduling In Energy -efficient Distributed Mixed Fuzzy Welding Shop)

150-151页

查看更多>>摘要：2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Robotics. Acc ording to news reporting out of Wuhan, People's Republic of China, by NewsRx edi tors, research stated, "Due to the impact of economic globalization, distributed welding shop has become prevalent in real-world manufacturing systems. Moreover , focusing on human-centric, sustainable and resilient industry, Industry 5.0 pu ts more emphasis on human-robot collaboration (HRC) for its merit in promoting s ystem flexibility and adaptability." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Key Research and Development Program Project in Hubei Provin ce. Our news journalists obtained a quote from the research from the China Universit y of Geosciences, "However, owing to the instability of human performance, it be comes necessary to employ fuzzy processing time to simulate practical human prod uction. In the context of Industry 5.0, HRC scheduling in distributed mixed fuzz y welding shop is worth exploring, but no related research on this problem is re ported. Thus, to address this research gap, this paper investigates a human-robo t collaboration energy-efficient distributed mixed fuzzy welding shop scheduling problem (EDMFWSP-HRC), aiming to minimize makespan and total energy consumption (TEC). To solve this issue, a self-learning memetic algorithm (SLMA) is propose d. In SLMA, a hybrid initialization is designed to yield a high-quality initial population. A genetic operator is proposed to improve the exploration capability . A self-learning variable neighborhood search (SLVNS), which hybridizes Q-learn ing and VNS, is developed to enhance the exploitation capability. A resource adj ustment strategy is presented to further optimize TEC. Additionally, to validate the effectiveness of the proposed SLMA, extensive experimental comparisons with 5 other optimization algorithms are conducted. Experimental results illustrate that SLMA outperforms its competitors. Note to Practitioners-Owing to the widesp read presence in manufacturing systems, distributed welding shop has attracted c onsiderable attention in both industry and academia. In the context of Industry 5.0, the incorporation of human-robot collaboration (HRC) scheduling in distribu ted welding shop can promote system productivity and flexibility. Meanwhile, due to the instability of human performance, employing fuzzy processing time to sim ulate human production more aligns with the practical manufacturing scenario. Th us, this paper investigates a human-robot collaboration energy-efficient distrib uted mixed fuzzy welding shop scheduling problem (EDMFWSP-HRC). This problem mod el can be utilized in many welding manufacturing enterprises with HRC production mode. To solve this problem, we design a self-learning memetic algorithm (SLMA) to minimize both makespan and total energy consumption (TEC). The design of all components in SLMA is based on the characteristics of problem. The SLMA can off er the low-energy and high-efficiency schedules for practitioners."

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Data from Nanyang Technological University Provide New Insights into Machine Lea rning (Predicting the Boron Removal of Reverse Osmosis Membranes Using Machine L earning)

151-152页

查看更多>>摘要：Current study results on Machine Learn ing have been published. According to news reporting originating from Singapore, Singapore, by NewsRx correspondents, research stated, "Reverse osmosis (RO) is a key technology for seawater desalination, but boron removal remains challengin g due to the relatively low and varying boron rejection of RO membranes. This st udy explored the use of machine learning (ML) to develop predictive models for b oron removal of RO membranes." Financial support for this research came from Nanyang Technological University f or the Presidential Postdoctoral Fellowship.

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Researcher from University of Adelaide Publishes Findings in Machine Learning (D omain Adaptation for Satellite-Borne Multispectral Cloud Detection)

152-153页

查看更多>>摘要：Current study results on artificial in telligence have been published. According to news originating from Adelaide, Aus tralia, by NewsRx correspondents, research stated, "The advent of satellite-born e machine learning hardware accelerators has enabled the onboard processing of p ayload data using machine learning techniques such as convolutional neural netwo rks (CNNs)." Funders for this research include Smartsat Crc. Our news journalists obtained a quote from the research from University of Adela ide: "A notable example is using a CNN to detect the presence of clouds in the m ultispectral data captured on Earth observation (EO) missions, whereby only clea r sky data are downlinked to conserve bandwidth. However, prior to deployment, n ew missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missio ns will underperform when deployed to process the data on the new missions. This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and fu ture missions. In this paper, we address the domain gap problem in the context o f onboard multispectral cloud detection."

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Data on Intelligent Transport Systems Discussed by Researchers at Nottingham Tre nt University [The Accessibility of Public Electric Vehicle ( Ev) Charging Infrastructure: Evidence From the Cities of Nottingham and Frankfur t]

153-154页

查看更多>>摘要：2024 OCT 09 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Investigators publish new report on Transportatio n - Intelligent Transport Systems. According to news reporting originating in No ttingham, United Kingdom, by NewsRx journalists, research stated, "The distribut ion of public electric vehicle (EV) charging infrastructure is a widespread appr oach for promoting EV adoption and decarbonising transportation. A significant a mount of literature explores the distribution of EV charging points at a country scale, but there is a lack of studies focusing on a district scale." Financial supporters for this research include Horizon 2020, European Union (EU) . The news reporters obtained a quote from the research from Nottingham Trent Univ ersity, "This study aims to contribute to this gap by gaining insights into the distribution of EV charging points per district within cities, such as Nottingha m and Frankfurt. The study investigates the current distribution of EV charging points across 38 postcode districts in Frankfurt and 9 postcode districts in Not tingham, using geographical data analysis and a linear regression approach. The following factors in response to the number of EV charging points per postcode d istrict (ZIP code) are examined: the percentage of apartment buildings/floor are a ratio, the availability of amenities, population, charging capacity (kW), area size, strategic approaches, including policy goals and principles. The results reveal disparities in access to EV charging infrastructure across districts and underscore the importance of expanding EV charging networks not only in district s located near urban centres or those with high availability of amenities but al so ensuring that users without home charging options are not left behind."

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